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Comparison between Regression Models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in River Water Quality Prediction

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dc.citedby2
dc.contributor.authorNajwa Mohd Rizal N.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorMnzool M.en_US
dc.contributor.authorElnaim B.M.E.en_US
dc.contributor.authorMohammed A.O.Y.en_US
dc.contributor.authorKhayyat M.M.en_US
dc.contributor.authorid57880422800en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57852200500en_US
dc.contributor.authorid57212004492en_US
dc.contributor.authorid57880616800en_US
dc.contributor.authorid57218570964en_US
dc.date.accessioned2023-05-29T09:36:45Z
dc.date.available2023-05-29T09:36:45Z
dc.date.issued2022
dc.description.abstractBoth anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R2). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research conducted to predict TS and TSS in Langat River. Therefore, this is the first study to evaluate the water quality (TSS, TS, and DS) of Langat River using the aforementioned models (especially SVM and the six regression models). � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1652
dc.identifier.doi10.3390/pr10081652
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85137570511
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137570511&doi=10.3390%2fpr10081652&partnerID=40&md5=b81e0f1e65798e43e7bb1d1163e36475
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26793
dc.identifier.volume10
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleProcesses
dc.titleComparison between Regression Models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in River Water Quality Predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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